Encoder signal analysis has proven to be a novel and cost-effective tool for the health monitoring of rotating machinery. Nevertheless, how to effectively detect the potential fault utilizing encoder information, especially at an early stage, remains a challenging issue. In light of this limitation, an improved Gaussian process regression analysis is proposed for the weak fault detection of rotating machinery via encoder signal. In this article, the Gaussian process regression model is first introduced to estimate the instantaneous angular speed and its confidence interval. Subsequently, to improve the robustness of Gaussian process regression under weak fault conditions, a spectral density complex kernel is constructed through modeling the spectral density with a mixture of Gaussians. Finally, built upon the eigenvalue decomposition, the optimal inference approach of improved Gaussian process regression is proposed. Compared with other regression methods, the major contribution is that the new method not only enhances the weak fault-related features but also sets their confidence interval adaptively. Using the proposed improved Gaussian process regression, the interference components are suppressed, while the fault-related instantaneous angular speed outliers are accurately detected. In addition, the significance of fault can be quantitatively evaluated according to the confidence level of the improved Gaussian process regression. The simulated and experimental analyses manifest that the proposed improved Gaussian process regression method can effectively identify the early weak fault. It may offer an effective tool for early fault detection of rotating machinery in industrial applications.

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基于编码器的旋转机械弱故障检测使用改进的高斯过程回归

编码器信号分析已被证明是一种用于旋转机械健康监测的新颖且经济高效的工具。然而，如何利用编码器信息有效地检测潜在故障，尤其是在早期，仍然是一个具有挑战性的问题。针对这一限制，提出了一种改进的高斯过程回归分析方法，用于通过编码器信号检测旋转机械的弱故障。本文首先引入高斯过程回归模型来估计瞬时角速度及其置信区间。随后，为了提高弱故障条件下高斯过程回归的鲁棒性，通过混合高斯函数对谱密度进行建模，构建了谱密度复核。最后，基于特征值分解，提出了改进高斯过程回归的最优推理方法。与其他回归方法相比，主要贡献在于新方法不仅增强了弱故障相关特征，而且自适应地设置了它们的置信区间。使用所提出的改进高斯过程回归，干扰分量被抑制，同时与故障相关的瞬时角速度异常值被准确检测。此外，还可以根据改进的高斯过程回归的置信度来定量评价故障的显着性。仿真和实验分析表明，所提出的改进高斯过程回归方法能够有效识别早期弱故障。